Schedule (Spring 2018)

All deadlines are at 10pm on the date listed. This includes both homeworks and projects.

Dates and readings are subject to change, but if a change is made, an announcement will be made in class or via Piazza.

Weekly homework will be always be distributed by Friday night and due the following Thursday. Weeks without homework are marked on the schedule.

There is a 2 day late period for Projects (see syllabus).

The readings are to be done before lecture. Quizzes will cover the readings for that lecture (as well as any lecture prior).

Week (Date) Monday Readings Wednesday Readings Notes
1 (2018-08-27) No Lecture (Class hasn't started) Syllabus and Chapter 1 (Introduction); (Slides) Quiz 0 and Homework 0 are both not worth any points, but you should complete them.
2 (2018-09-03) No Lecture (Labor Day Holiday) Chapter 2 (Intelligent Agents); (Slides)
3 (2018-09-10) Chapter 3.1-4 (Uninformed Search); (Slides) Chapter 3.5-7 (Heuristic Search)
4 (2018-09-17) Chapter 5.1-3 (Adversarial Search) Chapter 6.1-2 (Constraint Satisfaction) Project 1 released (due 2018-09-25 10:00pm REVISED to 2018-09-28 10:00pm with no late days allowed)
5 (2018-09-24) Chapter 6.3-6 (Local Search) Superintelligence: The Idea That Eats Smart People
6 (2018-10-01) Chapter 4.1 (Local Search) and Essays on Genetic Programing The Control Problem: (Surprising Creativity); (Video on Stop Button); (Video on Governance Problem)
7 (2018-10-08) Chapter 24.1-2 (Computer Vision) and Three Videos (Filters, Sorbel Operator, Canny Edge) Chapter 24.3-7 (Computer Vision again) and One Video (Short TED Talk )
8 (2018-10-15) Neural Networks (Chapter 18.7) and Video and AI Discrimination Learning Neural Networks and Slaughterbots (Video 1 and Video 2 and Video 3)
9 (2018-10-22) Guest Lecture by Dr. Heather Douglas; Readings: (The Dark Side of Science), (How Should We Manage The Machines?), (AAAS Statement on Scientific Freedom & Responsibility), and the two articles in the Google Drive Chapter 26 (Philosophy)
10 (2018-10-29) Machine Learning (The First 6 Videos in this playlist); Confusion Matrices Chapter 18.1-3 (Learning Decision Trees) and (The Videos 7 through 11 in this playlist) Project 2 is due at 10pm on 2018-11-15; no new homework this week due to project.
11 (2018-11-05) Other Methods: KNN (Two Videos: Fun and Formal; and Chapter 18.8.1); SVMs: Two Videos (Fun and Formal) and Chapter 18.9 Ensemble Learning: (Three Videos: Random Forests, Ensemble Learners, and Bagging), (Blogpost on Ensemble Learning and Jupyter Notebook), and Chapter 18.10.
12 (2018-11-12) Game Theory (Single Move): Intro Video, Example Video, and Chapter 17.5.1 Game Theory (Repeated Games): Fun Tit-For-Tat Video, Demonstration Game and Chapter 17.5.2 Project 3 (Seminar) is due 2018-12-04 10PM; Project 4 (Prisoner's Dilemma) is due 2018-12-04 10PM
13 (2018-11-19) Auctions: Intro Video and Chapter 17.6 Voting Theory (Video Playlist and Accompanying Document)
14 (2018-11-26) Reinforcement Learning: Intro Video and Blog 1 and Blog 2 Sci-kit-learn Tutorial 1 and Tutorial 2
15 (2018-12-03) Natural Language Processing: Intro Video, Bootcamp Video, and Vocab Video Applications: AWS Comprehend, TensorFlow, TensorFlow Image Classifier, and Jobs
Finals Week (2018-12-10) No Lecture No Lecture

Honors Project

To receive honors credit, you must propose a project. Something involving the use of AI either in theory or practice. For instance, using AI in an unrelated research project you are doing. Or, writing a review paper on a particular sub-discipline of AI. Something that at least requires 10-15 hours of work focused on AI. Generally, as long as we both agree on the requirements beforehand, I'm open to proposals.

Note: To receive the honors option, you will need to earn a 3.5 or a 4.0 in the class as well.